Building an Artificial Stock Market Populated by Reinforcement-Learning Agents
Tomas Ramanauskas () and
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Tomas Ramanauskas: Bank of Lithuania
Aleksandras Rutkauskas: Vilnius Gediminas Technical University
No 6, Bank of Lithuania Working Paper Series from Bank of Lithuania
In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup.
Keywords: agent-based financial modelling; artificial stock market; complex dynamical system; emergent properties; market efficiency; agent heterogeneity; reinforcement learning (search for similar items in EconPapers)
JEL-codes: G10 G11 G14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp
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